Image registration is the process of placing multiple images, which may have different coordinate systems corresponding thereto, into a common coordinate system. When registering images, mathematical functions, referred to as transforms, are computed and employed to transform the images into the common coordinate system. To perform such task, a base image is selected from amongst a set of images, where the base image is associated with a particular coordinate system (e.g., the common coordinate system). Subsequently, other images in the set of images are registered with the base image, thereby associating each image in the set of images with the common coordinate system.
When performing image registrations, one of two registering techniques is generally employed: 1) feature-based registration; or 2) optimization-based registration. In feature-based registration, features are extracted from images using a suitable feature extraction technique, and, for instance, a feature that exists in numerous images is identified. The location of such feature is identified 1) in each image in which the feature is found to exist; and 2) in a respective coordinate system of an image from which the feature is extracted. A transform may then be computed as a function of the locations of the feature across the images. It is recognized, however, that image registration can be complex for large image collections, where images therein include numerous features. Additionally, an assumption in any registration is that features or regions in the imagery maintain the same spatial relationship with respect to one another in the scene over time. Also, general distortion of the imagery (as is commonly present in imaging systems) generally precludes accurate registration unless it is taken into account during the registration process.
Optimization-based registration minimizes a difference metric that operates on image values directly. An optimization function iteratively adjusts a transform for an input (moving) image relative to the base (stationary) image, evaluates the accuracy of the transform using a metric that indicates an amount of correlation between the image and the base image, and ceases operation when the accuracy of the transform meets a threshold or the number of iterations meets a threshold. Optimization-based registration is typically more computationally intensive than feature-based methods.